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          开始学习urca包和vars包
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             开始学习urca包和vars包
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               2006年11月29日 下午9:22
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              <p>
               vars包主要为向量自回归模型理论的实现
               <br/>
               功能主要为：模型滞后阶数选择，估计，granger非因果检验，方差分解，脉冲相应分析，结构VAR模型分析等。
               <br/>
               ucra包主要为单位根协整理论的实现
               <br/>
               功能主要为：单位根检验，ADF，PP,KPSS,NP,PP,ERS检验
               <br/>
               johensen的协整系统极大似然分析。
              </p>
              <p>
               有人对这方面感兴趣吗？
               <br/>
               一起来学啊！
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            <div class="bbp-reply-header" id="post-213792">
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               2006年11月30日 上午4:33
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              <p>
               我现在正整理Zelig包，目前来不及学习这两个，但心理上非常支持你整理这两个包。
               <br/>
               不过，我建议你以一个包来建一个主题，把包的知识点及学习的体会及遇到的困难贴出，大家一起学习和交流。
               <br/>
               如果两个包放在同一主题下，有时容易搞混了。
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               2006年11月30日 上午4:52
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               <p>
                [b]引用第0楼[i]anning189[/i]于[i]2006-11-30 12:22[/i]发表的“开始学习urca包和vars包”[/b]:
                <br/>
                vars包主要为向量自回归模型理论的实现
                <br/>
                功能主要为：模型滞后阶数选择，估计，granger非因果检验，方差分解，脉冲相应分析，结构VAR模型分析等。
                <br/>
                ucra包主要为单位根协整理论的实现
                <br/>
                功能主要为：单位根检验，ADF，PP,KPSS,NP,PP,ERS检验
                <br/>
                johensen的协整系统极大似然分析。
                <br/>
                …….
               </p>
              </blockquote>
              <p>
               到时候一定向你请教，我对时间序列方面所知太少了，缺乏实际的项目经验
              </p>
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               2006年11月30日 上午11:46
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               我正在关注面板单根和协整问题，但缺乏实践经验
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               2006年11月30日 下午12:41
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               这个我要报名！我也正在研究：）时序的东西还是有点难啊，主要是本科的时候没学好，现在补起来感到很费劲，而且时序理论很多地方又比较偏数学，每次看到谱分析、滤子之类的东西我就心里发毛……
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               2006年11月30日 下午1:46
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                [b]引用第4楼[i]谢益辉[/i]于[i]2006-11-30 20:41[/i]发表的“”[/b]:
                <br/>
                这个我要报名！我也正在研究：）时序的东西还是有点难啊，主要是本科的时候没学好，现在补起来感到很费劲，而且时序理论很多地方又比较偏数学，每次看到谱分析、滤子之类的东西我就心里发毛……
               </p>
              </blockquote>
              <p>
               发现到处都是数学帝国主义的地盘了，尤其变量多了，要用超级大的矩阵方式表达的时候，我就开始有点头晕了，只怪以前矩阵论这本书没有学好，你那边有相关专门讲矩阵论的书么？
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            <div class="bbp-reply-header" id="post-213832">
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               2006年11月30日 下午2:39
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              <p>
               其实矩阵的东西也不是很深
               <br/>
               基本运算+ – * /，求逆，分块，各种分解，这些基本就ok了。
               <br/>
               推荐看看hamilton的时间序列分析，要看原版，翻译的那本不太爽。
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               2006年12月1日 上午1:26
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              <p>
               个人学习经济学的一些看法：
               <br/>
               1）以实证分析的角度看：学经济学－－－－必学计量经济（没有计量的经济不能说是主流经济学）－－－－学计量经济必学时间序列（所谓高等计量经济主要是时间序列部分，如VAR，单位根计量经济；panel data的高级部分无外也是面板单位根，面板协整）－－－－学时间序列与计量经济必学以测度论为基础的概率论（现代所谓的高等时间序列分析无外单位根与协整理论，而作为单位根理论基础的泛函中心极限定理来自与概率论中的鞅论，虽然现有教材在叙述该定理时未涉及鞅，但是那些都是讲了个皮毛，不是很严格，也很难继续理论上的发展）－－－－同时必学高等数理统计（这是计量经济学科大厦的基石，不学怎么行？）－－－－学习以上内容必需有以下基础知识：数学分析，矩阵论－－－－计量经济学家必须懂编程（将自己以上的理论进行实证，不能实证价值就显现不出了，毕竟经济学家比较注重现实，国外经济学家很多都自己编程，如hamilton，anderws等等，正如Cribari-Neto and Zarkos (2003)所说，现代应用研究人员已经无法忍受等待新方法进入软件包的漫长过程，而不得不自己编程。首选软件别无其他R，好处不必列举）
               <br/>
               2）以理论分析的角度看
               <br/>
               实证分析的主要目的是检验理论是否符合事实，那么理论来自于哪呢？
               <br/>
               学经济学－－－－必学所谓西方宏微观经济（基础）－－－－必学动态经济学（现代经济学主流是动态经济学，看看国外顶尖经济学杂志可知。）主要学以下几个方面a：微分方程及其定性分析b：动态优化理论c：随机动态优化
               <br/>
               d：混沌理论。个人认为混沌理论仅仅是一种思潮而已，应该把重点集中在b＆c上。
              </p>
              <p>
               ps：具备了以上两个大方面的知识并有自己的注重点，相信可以成为并能成为一名优秀的经济学家了。
              </p>
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               2006年12月1日 上午4:09
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               9 楼
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               谢益辉
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               站长
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              <p>
               这些看法很具有指导性，赞一个！
              </p>
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            <div class="bbp-reply-header" id="post-213890">
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               2006年12月1日 上午7:52
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               10 楼
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              <p>
               看了anning189的分析，作为一个非数学专业方面的人，痛苦啊
               <br/>
               那么多基础知识要去补，等我补上来，你们都进入共产主义了
              </p>
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               2006年12月2日 上午3:20
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              <p>
               ##vars pkg##
               <br/>
               加载这个包需要以下几个包的支持
               <br/>
               MASS; strucchange; zoo
               <br/>
               ##建立一个VAR模型
               <br/>
               对于平稳序列可以建立VAR模型，平稳性可由单位根检验判断
               <br/>
               VAR模型每个方程含有同样的解释变量，因此可以OLS估计，当然亦可MLE，见Hamilton1994
               <br/>
               该包即采用OLS估计
               <br/>
               library("vars")
               <br/>
               data(Canada) ##加拿大宏观经济变量数据
               <br/>
               var.const=VAR(Canada, p = 2, type = "const")#估计一个带有常数项的VAR
               <br/>
               &gt; args(VAR)
               <br/>
               function (y, p = 1, type = c("const", "trend", "both", "none"))
               <br/>
               y为原始变量矩阵；p为给定滞后阶数；type给定系统是否含有常数，时间，常数+时间，nothing。
               <br/>
               返回一个varest类，包含一些估计结果：系数估计，残差等。
               <br/>
               plot（var.const）#输出每个内生变量拟和图，残差图，残差ACF，PACF图。
              </p>
              <p>
               A.const=A(var.const)
               <br/>
               函数A（）输入一个varest类，即VAR估计结果，讲返回系统系数估计矩阵（不包含确定项）
               <br/>
               B.const=B(var.const)
               <br/>
               函数B（）输入一个varest类，即VAR估计结果，讲返回系统系数估计矩阵（所有系数）
              </p>
              <p>
               var.lag=VARselect(Canada, lag.max = 5, type="const")
               <br/>
               function (y, lag.max = 10, type = c("const", "trend", "both",     "none"))
               <br/>
               该函数用以选择VAR滞后阶数，y为原始数据矩阵，lag.max为最大滞后阶数
               <br/>
               返回AIC、SC、HQ信息准则值，以及预测残差和等信息以判定滞后阶数
              </p>
              <p>
               因果关系检验
               <br/>
               目前国内现有期刊水平(就我看到的杂志如：数量经济技术经济研究，经济研究)都是在做双变量granger非因果检验。vars包直接做多变量间因果分析。
               <br/>
               &gt; args(causality)
               <br/>
               function (x, cause = NULL)
               <br/>
               x为VAR估计结果，cause为原因变量
               <br/>
               检验为两种形式:
               <br/>
               1)F test 2)Word检验
               <br/>
               &gt; causality(var.const, cause = c("e","U"))
               <br/>
               $Granger
              </p>
              <p>
               Granger causality: e U do not Granger-cause prod rw
              </p>
              <p>
               data:  VAR object var.const
               <br/>
               F-Test = 4.2545, df1 = 8, df2 = 292, p-value = 7.512e-05
              </p>
              <p>
               $Instant
              </p>
              <p>
               H0: No instantaneous causality between: e U and prod rw
              </p>
              <p>
               data:  VAR object var.const
               <br/>
               Chi^2 = 2.5822, df = 4, p-value = 0.63
              </p>
              <p>
               详细理论上的东西见参考文献吧
               <br/>
               不过在vars包的pdf文件参考文献中由本书挺吸引人的
               <br/>
               Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer,
               <br/>
               ew York.
               <br/>
               要是能搞到这本书就爽了。
               <br/>
               人大的兄弟们，你们那图书馆有吗？
              </p>
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               2006年12月2日 上午3:54
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              <p>
               Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer,
               <br/>
               关于这本书的介绍，看了，心脏都快跳出来了！！！！
               <br/>
               Table of contents
               <br/>
               1 Introduction
              </p>
              <p>
               1.1 Objectives of Analyzing Multiple Time Series
               <br/>
               1.2 Some Basics
               <br/>
               1.3 Vector Autoregressive Processes
               <br/>
               1.4 Outline of the Following Chapters
               <br/>
               Part 1 Finite Order Vector Autoregressive Processes
              </p>
              <p>
               2 Stable Vector Autoregressive Processes
              </p>
              <p>
               2.1 Basic Assumptions and Properties of VAR Processes
               <br/>
               2.1.1 Stable VAR(p) Processes
               <br/>
               2.1.2 The Moving Average Representation of a VAR Process
               <br/>
               2.1.3 Stationary Processes
               <br/>
               2.1.4 Computation of Autocovariates and Autocorrelations of Stable VAR Processes
               <br/>
               2.2 Forecasting
               <br/>
               2.2.1 The Loss Function
               <br/>
               2.2.2 Point Forecasts
               <br/>
               2.2.3 Interval Forecasts and Forecast Regions
               <br/>
               2.3 Structural Analysis with VAR Models
               <br/>
               2.3.1 Granger-Causality, Instantaneous Causality, and Multi-Step Causality
               <br/>
               2.3.2 Impulse Response Analysis
               <br/>
               2.3.3 Forecast Error Variance Decomposition
               <br/>
               2.3.4 Remarks on the Interpretation of VAR Models
               <br/>
               2.4 Exercises
               <br/>
               3 Estimation of Vector Autoregressive Processes
              </p>
              <p>
               3.1 Introduction
               <br/>
               3.2 Multivariate Least Squares Estimation
               <br/>
               3.2.1 The Estimator
               <br/>
               3.2.2 Asymptotic Properties of the Least Squares Estimator
               <br/>
               3.2.3 An Example
               <br/>
               3.2.4 Small Sample Properties of the LS Estimator
               <br/>
               3.3 Least Squares Estimation with Mean-Adjusted Data and Yule-Walker Estimation
               <br/>
               3.3.1 Estimation when the Process Mean is Known
               <br/>
               3.3.2 Estimation of the Process Mean
               <br/>
               3.3.3 Estimation with Unknown Process Mean
               <br/>
               3.3.4 The Yule-Walker Estimator
               <br/>
               3.3.5 An Example
               <br/>
               3.4 Maximum Likelihood Estimation
               <br/>
               3.4.1 The Likelihood Function
               <br/>
               3.4.2 The ML Estimators
               <br/>
               3.4.3 Properties of the ML Estimators
               <br/>
               3.5 Forecasting with Estimated Processes
               <br/>
               3.5.1 General Assumptions and Results
               <br/>
               3.5.2 The Approximate MSE Matrix
               <br/>
               3.5.3 An Example
               <br/>
               3.5.4 A Small Sample Investigation
               <br/>
               3.6 Testing for Causality
               <br/>
               3.6.1 A Wald Test for Granger-Causality
               <br/>
               3.6.2 An Example
               <br/>
               3.6.3 Testing for Instantaneous Causality
               <br/>
               3.6.4 Testing for Multi-Step Causality
               <br/>
               3.7 The Asymptotic Distributions of Impulse Responses and Forecast Error Variance Decompositions
               <br/>
               3.7.1 The Main Results
               <br/>
               3.7.2 Proof of Proposition 3.6
               <br/>
               3.7.3 An Example
               <br/>
               3.7.4 Investigating the Distributions of the Impulse Responses by Simulation Techniques
               <br/>
               3.8 Exercises
               <br/>
               3.8.1 Algebraic Problems
               <br/>
               3.8.2 Numerical Problems
               <br/>
               4 VAR Order Selection and Checking the Model Adequacy
              </p>
              <p>
               4.1 Introduction
               <br/>
               4.2 A Sequence of Tests for Determining the VAR Order
               <br/>
               4.2.1 The Impact of the Fitted VAR Order on the Forecast MSE
               <br/>
               4.2.2 The Likelihood Ratio Test Statistic
               <br/>
               4.2.3 A Testing Scheme for VAR Order Determination
               <br/>
               4.2.4 An Example
               <br/>
               4.3 Criteria for VAR Order Selection
               <br/>
               4.3.1 Minimizing the Forecast MSE
               <br/>
               4.3.2 Consistent Order Selection
               <br/>
               4.3.3 Comparison of Order Selection Criteria
               <br/>
               4.3.4 Some Small Sample Simulation Results
               <br/>
               4.4 Checking the Whiteness of the Residuals
               <br/>
               4.4.1 The Asymptotic Distributions of the Autocovariances and Autocorrelations of a White Noise Process
               <br/>
               4.4.2 The Asymptotic Distributions of the Residual Autocovariances and Autocorrelations of an Estimated VAR Process
               <br/>
               4.4.3 Portmanteau Tests
               <br/>
               4.4.4 Lagrange Multiplier Tests
               <br/>
               4.5 Testing for Normality
               <br/>
               4.5.1 Tests for Nonnormality of a Vector White Noise Process
               <br/>
               4.5.2 Tests for Nonnormality of a VAR Process
               <br/>
               4.6 Tests for Structural Change
               <br/>
               4.6.1 Chow Tests
               <br/>
               4.6.2 Forecast Tests for Structural Change
               <br/>
               4.7 Exercises
               <br/>
               4.7.1 Algebraic Problems
               <br/>
               4.7.2 Numerical Problems
               <br/>
               5 VAR Processes with Parameter Constraints
              </p>
              <p>
               5.1 Introduction
               <br/>
               5.2 Linear Constraints
               <br/>
               5.2.1 The Model and the Constraints
               <br/>
               5.2.2 LS, GLS, and EGLS Estimation
               <br/>
               5.2.3 Maximum Likelihood Estimation
               <br/>
               5.2.4 Constraints for Individual Equations
               <br/>
               5.2.5 Restrictions for the White Noise Covariance Matrix
               <br/>
               5.2.6 Forecasting
               <br/>
               5.2.7 Impulse Response Analysis and Forecast Error Variance Decompositions
               <br/>
               5.2.8 Specification of Subset VAR Models
               <br/>
               5.2.9 Model Checking
               <br/>
               5.2.10 An Example
               <br/>
               5.3 VAR Processes with Nonlinear Parameter Restrictions
               <br/>
               5.4 Bayesian Estimation
               <br/>
               5.4.1 Basic Terms and Notation
               <br/>
               5.4.2 Normal Priors for the Parameters of a Gaussian VAR Process
               <br/>
               5.4.3 The Minnesota or Litterman Prior
               <br/>
               5.4.4 Practical Considerations
               <br/>
               5.4.5 An Example
               <br/>
               5.4.6 Classical versus Bayesian Interpretation of α in Forecasting and Structural Analysis
               <br/>
               5.5 Exercises
               <br/>
               5.5.1 Algebraic Exercises
               <br/>
               5.5.2 Numerical Problems
               <br/>
               Part 2 Cointegrated Processes
              </p>
              <p>
               6 Vector Error Correction Models
              </p>
              <p>
               6.1 Integrated Processes
               <br/>
               6.2 VAR Processes with Integrated Variables
               <br/>
               6.3 Cointegrated Processes, Common Stochastic Trends, and Vector Error Correction Models
               <br/>
               6.4 Deterministic Terms in Cointegrated Processes
               <br/>
               6.5 Forecasting Integrated and Cointegrated Variables
               <br/>
               6.6 Causality Analysis
               <br/>
               6.7 Impulse Response Analysis
               <br/>
               6.8 Exercises
               <br/>
               7 Estimation of Vector Error Correction Models
              </p>
              <p>
               7.1 Estimation of a Simple Special Case VECM
               <br/>
               7.2 Estimation of General VECMs
               <br/>
               7.2.1 LS Estimation
               <br/>
               7.2.2 EGLS Estimation of the Cointegration Parameters
               <br/>
               7.2.3 ML Estimation
               <br/>
               7.2.4 Including Deterministic Terms
               <br/>
               7.2.5 Other Estimation Methods for Cointegrated Systems
               <br/>
               7.2.6 An Example
               <br/>
               7.3 Estimating VECMs with Parameter Restrictions
               <br/>
               7.3.1 Linear Restrictions for the Cointegration Matrix
               <br/>
               7.3.2 Linear Restrictions for the Short-Run and Loading Parameters
               <br/>
               7.3.3 An Example
               <br/>
               7.4 Bayesian Estimation of Integrated Systems
               <br/>
               7.4.1 The Model Setup
               <br/>
               7.4.2 The Minnesota or Litterman Prior
               <br/>
               7.4.3 An Example
               <br/>
               7.5 Forecasting Estimated Integrated and Cointegrated Systems
               <br/>
               7.6 Testing for Granger-Causality
               <br/>
               7.6.1 The Noncausality Restrictions
               <br/>
               7.6.2 Problems Related to Standard Wald Tests
               <br/>
               7.6.3 A Wald Test Based on a Lag Augmented VAR
               <br/>
               7.6.4 An Example
               <br/>
               7.7 Impulse Response Analysis
               <br/>
               7.8 Exercises
               <br/>
               7.8.1 Algebraic Exercises
               <br/>
               7.8.2 Numeric Exercises
               <br/>
               8 Specification of VECMs
              </p>
              <p>
               8.1 Lag Order Specification
               <br/>
               8.2 Testing for the Rank of Cointegration
               <br/>
               8.2.1 A VECM without Deterministic Terms
               <br/>
               8.2.2 A Nonzero Mean Term
               <br/>
               8.2.3 A Linear Trend
               <br/>
               8.2.4 A Linear Trend in the Variables and Not in the Cointegration Relations
               <br/>
               8.2.5 Summary of Results and Other Deterministic Terms
               <br/>
               8.2.6 An Example
               <br/>
               8.2.7 Prior Adjustment of Deterministic Terms
               <br/>
               8.2.8 Choice of Deterministic Terms
               <br/>
               8.2.9 Other Approaches to Testing for teh Cointegrating Rank
               <br/>
               8.3 Subset VECMs
               <br/>
               8.4 Model Diagnostics
               <br/>
               8.4.1 Checking for Residual Autocorrelation
               <br/>
               8.4.2 Testing for Nonnormality
               <br/>
               8.4.3 Tests for Structural Change
               <br/>
               8.5 Exercises
               <br/>
               8.5.1 Algebraic Exercises
               <br/>
               8.5.2 Numerical Exercises
               <br/>
               Part 3 Structural and Conditional Models
              </p>
              <p>
               9 Structural VARs and VECMs
              </p>
              <p>
               9.1 Structural Vector Autoregressions
               <br/>
               9.1.1 The A-Model
               <br/>
               9.1.2 The B-Model
               <br/>
               9.1.3 The AB-Model
               <br/>
               9.1.4 Long-Run Restrictions à la Blanchard-Quah
               <br/>
               9.2 Structural Vector Error Correction Models
               <br/>
               9.3 Estimation of Structural Parameters
               <br/>
               9.3.1 Estimating SVAR Models
               <br/>
               9.3.2 Estimating Structural VECMs
               <br/>
               9.4 Impulse Response Analysis and Forecast Error Variance
               <br/>
               9.5 Further Issues
               <br/>
               9.6 Exercises
               <br/>
               9.6.1 Algebraic Problems
               <br/>
               9.6.2 Numerical Problems
               <br/>
               10 Systems of Dynamic Simultaneous Equations
              </p>
              <p>
               10.1 Background
               <br/>
               10.2 Systems with Unmodelled Variables
               <br/>
               10.2.1 Types of Variables
               <br/>
               10.2.2 Structural Form, Reduced Form, Final Form
               <br/>
               10.2.3 Models with Rational Expectations
               <br/>
               10.2.4 Cointegrated Variables
               <br/>
               10.3 Estimation
               <br/>
               10.3.1 Stationary Variables
               <br/>
               10.3.2 Estimation of Models with I(1) Variables
               <br/>
               10.4 Remarks on Model Specification and Model Checking
               <br/>
               10.5 Forecasting
               <br/>
               10.5.1 Unconditional and Conditional Forecasts
               <br/>
               10.5.2 Forecasting Estimated Dynamic SEMs
               <br/>
               10.6 Multiplier Analysis
               <br/>
               10.7 Optimal Control
               <br/>
               10.8 Concluding Remarks on Dynamic SEMs
               <br/>
               10.9 Exercises
               <br/>
               Part 4 Infinite Order Vector Autoregressive Processes
              </p>
              <p>
               11 Vector Autoregressive Moving Average Processes
              </p>
              <p>
               11.1 Introduction
               <br/>
               11.2 Finite Order Moving Average Processes
               <br/>
               11.3 VARMA Processes
               <br/>
               11.3.1 The Pure MA and Pure VAR Representations of a VARMA Process
               <br/>
               11.3.2 A VAR(1) Representation of a VARMA Process
               <br/>
               11.4 The Autocovariances and Autocorrelations of a VARMA(p, q) Process
               <br/>
               11.5 Forecasting VARMA Processes
               <br/>
               11.6 Transforming and Aggregating VARMA Processes
               <br/>
               11.6.1 Linear Transformations of VARMA Processes
               <br/>
               11.6.2 Aggregation of VARMA Processes
               <br/>
               11.7 Interpretation of VARMA Models
               <br/>
               11.7.1 Granger-Causality
               <br/>
               11.7.2 Impulse Reponse Analysis
               <br/>
               11.8 Exercises
               <br/>
               12 Estimation of VARMA Models
              </p>
              <p>
               12.1 The Identification Problem
               <br/>
               12.1.1 Nonuniqueness of VARMA Representations
               <br/>
               12.1.2 Final Equations Form and Echelong Form
               <br/>
               12.1.3 Illustrations
               <br/>
               12.2 The Gaussian Likelihood Function
               <br/>
               12.2.1 The Likelihood Function of an MA(1) Process
               <br/>
               12.2.2 The MA(q) Case
               <br/>
               12.2.3 The VARMA(1,1) Case
               <br/>
               12.2.4 The General VARMA(p, q) Case
               <br/>
               12.3 Computation of the ML Estimates
               <br/>
               12.3.1 The Normal Equations
               <br/>
               12.3.2 Optimization Algorithms
               <br/>
               12.3.3 The Information Matrix
               <br/>
               12.3.4 Preliminary Estimation
               <br/>
               12.3.5 An Illustration
               <br/>
               12.4 Asymptotic Properties of the ML Estimators
               <br/>
               12.4.1 Theoretical Results
               <br/>
               12.4.2 A Real Data Example
               <br/>
               12.5 Forecasting Estimated VARMA Processes
               <br/>
               12.6 Estimated Impulse Responses
               <br/>
               12.7 Exercises
               <br/>
               13 Specification and Checking the Adequacy of VARMA
              </p>
              <p>
               13.1 Introduction
               <br/>
               13.2 Specification of the Final Equations Form
               <br/>
               13.2.1 A Specification Procedure
               <br/>
               13.2.2 An Example
               <br/>
               13.3 Specification of Echelon Forms
               <br/>
               13.3.1 A Procedure for Small Systems
               <br/>
               13.3.2 A Full Search Procedure Based on Linear Least Squares Computations
               <br/>
               13.3.3 Hannan-Kavalieris Procedure
               <br/>
               13.3.4 Poskitt’ Procedure
               <br/>
               13.4 Remarks on Other Specification Strategies for VARMA Models
               <br/>
               13.5 Model Checking
               <br/>
               13.2.1 LM Tests
               <br/>
               13.5.2 Residual Autocorrelations and Portmanteau Tests
               <br/>
               13.5.3 Prediction Tests for Structural Change
               <br/>
               13.6 Critique of VARMA Model Fitting
               <br/>
               13.7 Exercises
               <br/>
               14 Cointegrated VARMA Processes
              </p>
              <p>
               14.1 Introduction
               <br/>
               14.2 The VARMA Framework for I(1) Variables
               <br/>
               14.2.1 Levels of VARMA Models
               <br/>
               14.2.2 The Reverse Echelon Form
               <br/>
               14.2.3 The Error Correction Echelong Form
               <br/>
               14.3 Estimation
               <br/>
               14.3.1 Estimation of ARMARE Models
               <br/>
               14.3.2 Estimation of EC-ARMARE Models
               <br/>
               14.4 Specification of EC-ARMARE Models
               <br/>
               14.4.1 Specification of Kronecker Indices
               <br/>
               14.4.2 Specification of the Cointegrating Rank
               <br/>
               14.5 Forecasting Cointegrated VARMA Processes
               <br/>
               14.6 An Example
               <br/>
               14.7 Exercises
               <br/>
               14.7.1 Algebraic Exercises
               <br/>
               14.7.2 Numerical Exercises
               <br/>
               15 Fitting Finite Order VAR Models to Infinite Order Processes
              </p>
              <p>
               15.1 Background
               <br/>
               15.2 Multivariate Least Squares Estimation
               <br/>
               15.3 Forecasting
               <br/>
               15.3.1 Theoretical Results
               <br/>
               15.3.2 An Example
               <br/>
               15.4 Impulse Response Analysis and Forecast Error Variance Decompositions
               <br/>
               15.4.1 Asymptotic Theory
               <br/>
               15.4.2 An Example
               <br/>
               15.5 Cointegrated Infinite Order VARs
               <br/>
               15.5.1 The Model Setup
               <br/>
               15.5.2 Estimation
               <br/>
               15.5.3 Testing for the Cointegrating Rank
               <br/>
               15.6 Exercises
               <br/>
               Part 5 Time Series Topics
              </p>
              <p>
               16 Multivariate ARCH and GARCH Models
              </p>
              <p>
               16.1 Background
               <br/>
               16.2 Univariate GARCH Models
               <br/>
               16.2.1 Definitions
               <br/>
               16.2.2 Forecasting
               <br/>
               16.3 Multivariate GARCH Models
               <br/>
               16.3.1 Multivariate ARCH
               <br/>
               16.3.2 MGARCH
               <br/>
               16.3.3 Other Multivariate ARCH and GARCH Models
               <br/>
               16.4 Estimation
               <br/>
               16.4.1 Theory
               <br/>
               16.4.2 An Example
               <br/>
               16.5 Checking MGARCH Models
               <br/>
               16.5.1 ARCH-LM and ARCH-Portmanteau Tests
               <br/>
               16.5.2 LM and Portmanteau Tests for Remaining ARCH
               <br/>
               16.5.3 Other Diagnostic Tests
               <br/>
               16.5.4 An Example
               <br/>
               16.6 Interpreting GARCH Models
               <br/>
               16.6.1 Causality in Variance
               <br/>
               16.6.2 Conditional Moment Profiles and Generalized Impulse Responses
               <br/>
               16.7 Problems and Extensions
               <br/>
               16.8 Exercises
               <br/>
               17 Periodic VAR Processes and Intervention Models
              </p>
              <p>
               17.1 Introduction
               <br/>
               17.2 The VAR(p) Model with Time Varying Coefficients
               <br/>
               17.2.1 General Properties
               <br/>
               17.2.2 ML Estimation
               <br/>
               17.3 Periodic Processes
               <br/>
               17.3.1 A VAR Representation with Time Invariant Coefficients
               <br/>
               17.3.2 ML Estimation and Testing for Time Varying Coefficients
               <br/>
               17.3.3 An Example
               <br/>
               17.3.4 Bibliographical Notes and Extensions
               <br/>
               17.4 Intervention Models
               <br/>
               17.4.1 Interventions in the Intercept Model
               <br/>
               17.4.2 A Discrete Change in the Mean
               <br/>
               17.4.3 An Illustrative Example
               <br/>
               17.4.4 Extensions and References
               <br/>
               17.5 Exercises
               <br/>
               18 State Space Models
              </p>
              <p>
               18.1 Background
               <br/>
               18.2 State Space Models
               <br/>
               18.2.1 The Model Setup
               <br/>
               18.2.2 More General State Space Models
               <br/>
               18.3 The Kalman Filter
               <br/>
               18.3.1 The Kalman Filter Recursions
               <br/>
               18.3.2 Proof of the Kalman Filter Recursions
               <br/>
               18.4 Maximum Likelihood Estimation of State Space Models
               <br/>
               18.4.1 The Log-Likelihood Function
               <br/>
               18.4.2 The Identification Problem
               <br/>
               18.4.3 Maximization of the Log-Likelihood Function
               <br/>
               18.4.4 Asymptotic Properties of the ML Estimator
               <br/>
               18.5 A Real Data Example
               <br/>
               18.6 Exercises
               <br/>
               Appendix
              </p>
              <p>
               A Vectors and Matrices
              </p>
              <p>
               A.1 Basic Definitions
               <br/>
               A.2 Basic Matrix Operations
               <br/>
               A.3 The Determinant
               <br/>
               A.4 The Inverse, the Adjoint, and Generalized Inverses
               <br/>
               A.4.1 Inverse and Adjoint of a Square Matrix
               <br/>
               A.4.2 Generalized Inverses
               <br/>
               A.5 The Rank
               <br/>
               A.6 Eigenvalues and -vectors — Characteristic Values and Vectors
               <br/>
               A.7 The Trace
               <br/>
               A.8 Some Special Matrices and Vectors
               <br/>
               A.8.1 Idempotent and Nilpotent Matrices
               <br/>
               A.8.2 Orthogonal Matrices and Vectors and Orthogonal Complements
               <br/>
               A.8.3 Definite Matrices and Quadratic Forms
               <br/>
               A.9 Decomposition and Diagonalization of Matrices
               <br/>
               A.9.1 The Jordan Canonical Form
               <br/>
               A.9.2 Decomposition of Symmetric Matrices
               <br/>
               A.9.3 The Choleski Decomposition of a Positive Definite Matrix
               <br/>
               A.10 Partitioned Matrices
               <br/>
               A.11 The Kronecker Product
               <br/>
               A.12 The vec and vech Operators and Related Matrices
               <br/>
               A.12.1 The Operators
               <br/>
               A.12.2 Elimination, Duplication, and Commutation Matrices
               <br/>
               A.13 Vector and Matrix Differentiation
               <br/>
               A.14 Optimization of Vector Functions
               <br/>
               A.15 Problems
               <br/>
               B Multivariate Normal and Related Distributions
              </p>
              <p>
               B.1 Multivariate Normal Distributions
               <br/>
               B.2 Related Distributions
               <br/>
               C Stochastic Convergence and Asymptotic Distributions
              </p>
              <p>
               C.1 Concepts of Stochastic Convergence
               <br/>
               C.2 Order in Probability
               <br/>
               C.3 Infinite Sums of Random Variables
               <br/>
               C.4 Laws of Large Numbers and Central Limit Theorems
               <br/>
               C.5 Standard Asymptotic Properties of Estimators and Test Statistics
               <br/>
               C.6 Maximum Likelihood Estimation
               <br/>
               C.7 Likelihood Ratio, Lagrange Multiplier, and Wald Tests
               <br/>
               C.8 Unit Root Asymptotics
               <br/>
               C.8.1 Univariate Processes
               <br/>
               C.8.2 Multivariate Processes
               <br/>
               D Evaluating Properties of Estimators and Test Statistics by Simulation and Resampling Techniques
              </p>
              <p>
               D.1 Simulating a Multiple Time Series with VAR Generation Process
               <br/>
               D.2 Evaluating Distributions of Functions of Multiple Time Series by Simulation
               <br/>
               D.3 Resampling Methods
              </p>
             </div>
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               2006年12月2日 上午3:59
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               13 楼
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               anning189
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               人大图书馆真的有这本书，我晕，我们这落后，没有
              </p>
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            <div class="bbp-reply-header" id="post-214026">
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               2006年12月2日 上午10:10
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               14 楼
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               <img src="http://sdn.geekzu.org/avatar/1022d8e6ebc94e8f6bca9a86cebe312a?s=80&amp;d=monsterid&amp;r=g"/>
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              <a class="bbp-author-name" href="http://cos.name/cn/profile/1/" rel="nofollow" title="查看谢益辉的档案">
               谢益辉
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               站长
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              <p>
               我找了一下，我们图书馆没有这本书啊，只有该作者（1991年？）的一本Introduction to multiple time series analysis，内容好象也差不多：）
              </p>
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               2006年12月2日 下午12:01
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               15 楼
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               呵呵，我找到电子版了，新版和旧版差很多啊
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 </body>
</html>